Programmable multispectral illumination system for surgery and visualization of light-sensitive tissues

ABSTRACT

An observation system for viewing light-sensitive tissue includes an illumination system configured to illuminate the light-sensitive tissue, an imaging system configured to image at least a portion of the light-sensitive tissue upon being illuminated by the illumination system, and an image display system in communication with the imaging system to display an image of the portion of the light-sensitive tissue. The illumination system is configured to illuminate the light-sensitive tissue with a reduced amount of light within a preselected wavelength range compared to multispectral illumination light, and the image of the portion of the light-sensitive tissue is compensated for the reduced amount of light within the preselected frequency range to approximate an image of the light-sensitive tissue under the multispectral illumination.

CROSS-REFERENCE OF RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/387,950 filed on Jan. 30, 2012, the entire contents of which areincorporated herein by reference. This application claims priority toU.S. Provisional Application Nos. 61/231,519 filed Aug. 5, 2009 and61/325,647 filed Apr. 19, 2010 the entire contents of which are herebyincorporated by reference, and is a national stage application under 35U.S.C. § 371 of PCT/US2010/044596 filed Aug. 5, 2010, the entirecontents of which are incorporated herein by reference.

This invention was made with U.S. Government support of NSF CooperativeAgreement EEC9731478 and Grant No. 1 R01 EB 007969-01, awarded by theNIH. The U.S. Government has certain rights in this invention.

BACKGROUND 1. Field of Invention

The current invention relates to observation systems and methods ofimaging light-sensitive tissue, and more particularly to observationsystems and methods of imaging light-sensitive tissue with reducedpho-totoxicity.

2. Discussion of Related Art

Retinal microsurgery is one of the most demanding types of surgery. Thedifficulty stems from the microscopic dimensions of tissue planes andblood vessels in the eye, the delicate nature of the neurosensory retinaand the poor recovery of retinal function after injury. Manymicron-scale maneuvers are physically not possible for many retinalsurgeons due to inability to visualize the tissue planes, tremor, orinsufficient dexterity. To safely perform these maneuvers, microscopesare required to view the retina. A central issue for the surgeon is thecompromise between adequate illumination of retinal structures, and therisk of iatrogenic phototoxicity either from the operating microscope orendoilluminators, which are fiber-optic light sources that are placedinto the vitreous cavity to provide adequate illumination of the retinaduring delicate maneuvers.

Retinal phototoxicity from an operating microscope was first reported in1983 in patients who had undergone cataract surgery with intraocularlens implantation (McDonald, H., Irvine, A.: Light-induced maculopathyfrom the operating micro-scope in extracapsular cataract extraction andintraocular lens implantation. Ophthalmology 90, 945-951 (1983)).Retinal phototoxicity is now a well recognized potential complication ofany intraocular surgical procedure, and the frequency is reported tooccur from 7% to 28% of patients undergoing cataract surgery (Khwarg,S., Linstone, F., Daniels, S., Isenberg, S., Hanscom, T., Geoghegan, M.,Straatsma, B.: Incidence, risk factors, and morphology in operatingmicroscope light retinopathy. American Journal of Ophthalmology 103,255-263 (1987); Byrnes, G., Antoszyk, A., Mazur, D., Kao, T., Miller,S.: Photic maculopathy after extracapsular cataract surgery aprospective study. Ophthalmology 99, 731-738 (1992)). As a result, theInternational Commission on Non-Ionizing Radiation Protection (ICNIRP)now provides safety guidelines for illumination of the fundus in bothphakic and aphakic subjects (International Commission on Non-IonizingRadiation Protection: Guidelines on limits of exposure to broad-bandincoherent optical radiation (0.38 to 3). Health Phys. 73, 539-554(1997)). Blue wavelength and ultraviolet light induce the greatestdegree of retinal injury. In fact, in (van den Biesen, R., Berenschot,T., Verdaasdonk, R., van Weelden, H., van Norren, D.: Endoilluminationduring vitrectomy and phototoxicity thresholds. British Journal ofOphthalmology 84, 1372-1375 (2000)) it was found that commerciallyavailable light sources for endoillumination exceeded the ICNIRPguidelines for retinal damage by visible light within 3 minutes, and in9 of 10 sources, the safe exposure time was exceeded in less than 1minute. In vitrectomy for macular hole repair, up to 7% of the patientshave been reported to have experienced visually significantphototoxicity (Poliner, L., Tornambe, P.: Retinal pigment epitheliopathyafter macular hole surgery. Ophthalmology 99, 1671-1677 (1992); Michels,M., Lewis, H., Abrams, G., Han, D., Mieler, W., Neitz, J.: Macularpho-totoxicity caused by fiberoptic endoillumination during pars planavitrectomy. American Journal of Ophthalmol. 114, 287-292 (1992); Banker,A., Freeman, W., Kim, J., Munguia, D., Azen, S.: Vision-threateningcomplications of surgery for full-thickness macular holes. Ophthalmology104, 1442-1453 (1997)).

Phototoxicity can be either thermal or photochemical in nature fromexcessive ultraviolet (UV) or blue light toxicity. Ham et al. showed theaction spectrum or relative risk of UV or blue light toxicity when theretina was exposed to various wavelengths of light (Ham, W. J., Mueller,H., Ru olo, J. J., Guerry, D., Guerry, R.: Action spectrum for retinalinjury from near-ultraviolet radiation in the aphakic monkey. AmericanJournal of Ophthalmology 93, 299-306 (1982)). The action spectrum wasthen used to create a relative risk of phototoxicity associated with agiven wavelength of light.

The Aphakic Hazard Function describes the phototoxic potential ofretinal light exposure within and near the visible spectrum. As seenfrom the curve in FIG. 1, retinal phototoxicity occurs primarily atshort wavelengths, such as blue light. Red light has little to nophototoxic impact compared to blue light.

Current medical light sources attempt to limit phototoxicity by usingfilters to block wavelengths at the blue end of the visible spectrum.This approach has only limited usefulness, however, since blocking partof the visible spectrum hinders color rendition. Xenon is currently theillumination source of choice for retinal surgery. As shown on theAphakic Hazard Function diagram (FIG. 1)(Ohji, Masahito and Tano, Yasuo.Vitreo-retinal Surgery. “Chapter 7: New Instruments in Vitrectomy”.Berlin Heidelberg: Springer, 2007), a Xenon spectrum is fairly flat andhas substantial coverage within the hazardous blue wavelength range.Thus, the industry standard light source for retinal surgery may be asignificant health hazard for patients. The risk, while reduced, isstill significant for intraocular surgery. Given the advancing age ofthe population and increasing prevalence of retinal diseases, thereremains a need for further improvements aimed at reducing iatrogenicretinal phototoxicity.

SUMMARY

An observation system for viewing light-sensitive tissue according to anembodiment of the current invention includes an illumination systemconfigured to illuminate the light-sensitive tissue, an imaging systemconfigured to image at least a portion of the light-sensitive tissueupon being illuminated by the illumination system, and an image displaysystem in communication with the imaging system to display an image ofthe portion of the light-sensitive tissue. The illumination system isconfigured to illuminate the light-sensitive tissue with a reducedamount of light within a preselected wavelength range compared tomultispectral illumination light, and the image of the portion of thelight-sensitive tissue is compensated for the reduced amount of lightwithin the preselected frequency range to approximate an image of thelight-sensitive tissue under the multispectral illumination.

A method of displaying an image of light-sensitive tissue according toan embodiment of the current invention includes illuminating thelight-sensitive tissue with multispectral light for a first period oftime, imaging the light-sensitive tissue over the first period of timeupon being illuminated with the multispectral light, and displaying theimage of the light-sensitive tissue for a second period of time that islonger than the first period of time. The second period of time includesa period of time in which the light-sensitive tissue is free of themultispectral illumination, and the imaging the light-sensitive tissueincludes compensating for the period of time in which thelight-sensitive tissue is free of the multispectral illumination toapproximate an image of the light-sensitive tissue as it would appearhad it been under the multispectral illumination for the entire secondperiod of time.

BRIEF DESCRIPTION OF THE DRAWINGS

Further objectives and advantages will become apparent from aconsideration of the description, drawings, and examples.

FIG. 1 is a graph of human eye response, aphakic hazard, xenon and xenonwith yellow filter spectra.

FIG. 2 is a schematic illustration of an observation system for viewinglight-sensitive tissue according to an embodiment of the currentinvention.

FIG. 3 shows the LED and camera trigger waveforms used in one exampleaccording to an embodiment of the current invention.

FIG. 4 shows images demonstrating an example of video sequences showingone frame illuminated by white light for every four red light framesaccording to an embodiment of the current invention.

FIG. 5A shows an example of a non-white light image from a deviceaccording to an embodiment of the current invention and FIG. 5B showsthe image rendered by the naive algorithm. Notice that simply using theG and B channels from the last white frame does not generate good colorimages. This is particularly the case when there is motion in the scene.

FIG. 6A is an example of a non-white light image from a device accordingto an embodiment of the current invention, FIG. 6B shows a segmentedtool corresponding to FIG. 6A, and FIG. 6C shows a rendered image by ASRaccording to an embodiment of the current invention. Here, the tool iscorrectly estimated and image regions are updated in a coherent manner.

FIGS. 7A and 7B show evaluations of the proposed methods when varyingthe fraction of available white-light images, φ. The average error perpixel ((FIG. 7A) L2 norm (FIG. 7B) BV norm) is computed on imagesequences where ground truth is known. Both error metrics indicate thatASR (dashed line) provides accuracy gains over the naive approach (solidline).

FIG. 8 shows example image sequences from the device (φ=½) (top row) andcorresponding rendered sequence using ASR (bottom row) according to anembodiment of the current invention.

FIG. 9 shows intermediate steps using the AASR algorithm: (a) non-whiteimage provided by the device when L_(t)=0, (b) tool segmentation, (c)representation of M_(t) and (d) recolored image by AASR.

FIG. 10 provides a block diagram of the system according to anembodiment of the current invention.

FIG. 11A provides a plot of estimated phototoxicity levels andrecoloring error for both ASR and AASR according to embodiments of thecurrent invention. Notice that AASR is less phototoxic than ASR forevery recoloring error level.

FIG. 11B shows an example image sequence of a membrane peel according toan embodiment of the current invention: Ground Truth (top), white andnon-white illumination images triggered by AASR (middle) and AASR imagerecolorization (bottom).

DETAILED DESCRIPTION

Some embodiments of the current invention are discussed in detail below.In describing embodiments, specific terminology is employed for the sakeof clarity. However, the invention is not intended to be limited to thespecific terminology so selected. A person skilled in the relevant artwill recognize that other equivalent components can be employed andother methods developed without departing from the broad concepts of thecurrent invention. All references cited anywhere in this specificationare incorporated by reference as if each had been individuallyincorporated.

Some embodiments of the current invention include an illumination systemthat reduces the risks of phototoxicity for patients undergoingphotosensitive surgery. Typical white light illumination used duringsuch surgery may induce trauma in photosensitive tissues within thebody. The most striking example of this is vitreoretinal surgeryperformed on the retina. Being highly sensitive to light, the retina iseasily damaged by white light. This is of critical concern for surgeonsand patients, because a perfectly performed surgery may yet achieve poorresults in terms of patient vision due to phototoxic trauma incurredduring the procedure. Thus, a system that reduces the occurrence andrisk of phototoxicity during retinal surgery could have very significantand far-reaching impact.

Another aspect of some embodiments of the current invention includes amultispectral programmable light source that has the capability ofintegrating different spectral emissions in various ways to supportspecial purpose tasks. As an example, a surgeon may use a fluorescentdye as an aid to visualize an anatomical feature for performance of asurgical task. Such a dye could be activated by exposure to specificwavelengths of light. A light source that activates fluorescenceon-demand by selectively emitting these wavelengths would support such aprocedure. An additional example includes selective use of IR light toprovide a slightly different view of the retinal surface. Although IR isnot visible to the human eye, it is visible to the cameras used by theillumination system according to some embodiments of the currentinvention. Because IR penetrates more deeply into tissue than visiblewavelengths, its use may improve observation of anatomy lying just belowthe retinal surface. Furthermore, IR light has very low phototoxicityand may therefore double as an ultra-safe method of illuminatingphoto-sensitive tissues.

We present a novel observation system according to some embodiments ofthe current invention that can be used to significantly reduce theemission of highly toxic wavelengths over existing systems. Whilechanging the spectral composition of the illumination toward longerwavelengths could help reduce phototoxicity, we have created a newdevice according to some embodiments of the current invention whichcyclicly illuminates the retina using white light and less damagingnon-white light, allowing for maximal phototoxicity reduction.Consequently, images provided by this device are fully colored,monochromatic, or have varying intensities of different portions of thenormally visible “white light” spectrum.

To avoid visually straining a potential user (e.g., a surgeon) thisdevice can include an image recoloring scheme. Computer colorizationschemes have existed since the 70's (Museum of Broadcast Communication:Encyclopedia of Television (online),http://vvww.museum.tv/archives/etv/c/htmlc/colorization/colorization.htm)and have since been further developed (Skora, D., Burinek, J., Zra, J.:Unsupervised colorization of black and white car-toons. In: Int. Symp.NPAR, Annecy, pp. 121-127 (2004); Yatziv, L., Sapiro, G.: Fast image andvideo colorization using chrominance blending. IEEE Transactions onImage Processing 15, 1120-1129 (2006)). In general, however, suchsystems rely on a user to pre-select regions of the image thatcorrespond to specific colors, making them ill-suited for thisapplication. More recently, a time series analysis was proposed to modelthe retinal image scene (Sznitman, R., Lin, H., Manaswi, G., Hager, G.:Active background modeling: Ac-tors on a stage. In: InternationalConference on Computer Vision, Workshop on Visual Surveillance (2009)).This method however relies on having all visual cues (e.g. color andtexture) available at all times to maintain an accurate retina model. Toour knowledge, no previous work has focused on fusing images taken undervarying spectrum illumination to form continuous and coherent imagesequences.

Our approach to a low phototoxicity light source capitalizes on uniquecapabilities afforded through the use of video microscopy. Using videomicroscopy to indirectly observe retinal surgery, rather than viewingthe procedure directly through an optical microscope, allows white lightexposure to be reduced in at least the following ways, each of whichwill be described in more detail:

1. Camera Shutter Synchronization: Enables illumination only when thecamera shutter is open; disables illumination when the camera shuttercloses following capture of each video frame.

2. Multiplexed Spectrum Imaging: This technique involves changing thelight emission spectrum between successive video frames. Framesilluminated by white light are interleaved between frames illuminated byreduced phototoxicity light (e.g., red light) at a repeating interval.Tool tracking and background registration techniques are then used tomap color information from the most recent white frame (which appears infull-color) to all subsequently captured red frames (which appear inmono-color). By this method, the video feed is converted to full-colorfor all frames.

3. Color Companding of Phototoxic Wavelengths: This method performscolor companding of highly phototoxic light by reducing the intensity ofthe most harmful wavelengths in the emission spectrum and subsequentlyapplying a color boost model to the captured video image thatcomputationally boosts the color information corresponding to theattenuated wavelengths. In this way, a full-color image with normalcolor-balance can be rendered from an illumination spectrum having heavybias towards low phototoxic regions of the visible spectrum.

4. Adaptive Multispectral Imaging: This technique involves interleavingframes of light with different spectra (as in multiplexed spectrumimaging) or varying the intensity of phototoxic wavelengths (as in colorcompanding) or both in combination, in which a computer automaticallyvaries the fraction of white light to other frames or the relativefraction of phototoxic light based on processing of video imagescaptured during the procedure. For example, the computer mighttemporarily increase the ratio of white light images to red light framesif the scene is rapidly changing or a tool is moving rapidly across thebackground.

Any combination of each of these four techniques could also be used inother embodiments of the current invention. Furthermore, the generalconcepts of the current invention are not limited to only theseparticular embodiments.

FIG. 2 is a schematic illustration of an observation system 200 forviewing light-sensitive tissue 201 according to an embodiment of thecurrent invention. The observation system 200 includes an illuminationsystem 202 configured to illuminate the light-sensitive tissue 201, animaging system 204 configured to image at least a portion of thelight-sensitive tissue 201 upon being illuminated by the illuminationsystem 202, and an image display system 206 in communication with theimaging system 204 to display an image of the portion of thelight-sensitive tissue 201. The illumination system 202 is configured toilluminate the light-sensitive tissue 201 with a reduced amount of lightwithin a preselected frequency range compared to multispectralillumination light. For example, the illumination system can beconstructed to provide illumination light that uses less of a certainwavelength range of light, such as, but not limited to, toxic light,relative to light that normally would be provided to obtain a certainimage quality. This “less amount” can be relative to multispectralillumination that would ordinarily be performed, such as, but notlimited to, substantially white light illumination. Furthermore, such arelative reduction can be achieved by spectral, spatial and/or temporalillumination processes, for example. The image of the portion of thelight-sensitive tissue is compensated for the reduced amount of lightwithin the preselected frequency range to better approximate an image ofthe light-sensitive tissue 201 under said multispectral illumination.For example, some embodiments can include, but are not limited to,illuminating with light that is reduced in an amount of toxic lightrelative to illumination with substantially white light illumination andthen compensating the image to appear more like a white-lightilluminated image. The term “compensating” is intended to have a broadmeaning with respect to various embodiments. For example, thecompensation can include, but is not limited to, making an intermittentimage appear continuous and/or other processing to counter effects ofthe reduced relative amount of illumination in the preselectedwavelength range. The term “white light” is intended to have a broadmeaning to include multispectral light in the visible spectrum withspectral characteristics similar to those normally encountered incircumstances that permit normal color vision. It is not intended tomean an exact, particular spectral composition. Furthermore, the term“substantially white light” can include cases in which the light wouldactually appear off white with perhaps some slight color.

In the embodiment of FIG. 2, the illumination system 202 includes alight source 208 and a light source controller 210 constructed andarranged to control at least one of a spectral composition and intensityof light that illuminates the light-sensitive tissue 201. In theembodiment of FIG. 2, the light source controller 210 is an electroniccontrol module that is in communication with the light source 208 andthe imaging system 204. However, other embodiments could include otherways to control the light output of the illumination system 202 such asother types of light sources, mechanical choppers to chop anillumination beam and/or other types of optical components such asoptical filter, prisms and/or lenses. In some embodiments, theillumination system 202 can include and/or be coupled to other opticalcomponents to enable illumination of the light-sensitive tissue 201. Forexample, the illumination system can include an optical fiber 212attached to a light pipe 214 according to some embodiments of thecurrent invention. The illumination system 202 can include a fiber-opticcoupler 216 to couple light emitted from the light source 208 into theoptical fiber 212.

The imaging system 204 includes one or more imaging optical detectors218. In some embodiments, the imaging system may be adapted to attach toand/or include a stereoscopic video microscope 220, for example, as isillustrated schematically in FIG. 2. The imaging system 204 can bearranged to image the light-sensitive tissue 201 and can be incommunication with the light source controller 210 of the illuminationsystem 202. In some embodiments, the imaging system can include otherelectronic components to process image data from the imaging opticaldetectors. For example, the imaging system can include a PC or othercomputer 222 and/or other data processing and/or storage system toprocess imaging data from the imaging optical detectors 218. The PC orcomputer 222 can also be in communication with the illumination system202, such as with the light source controller 210 according to someembodiments of the current invention. The data communications channelsbetween the various components shown in FIG. 2 can be hard-wiredelectrical connections, optical connections and/or wireless connections,for example, or may use other data communications devices or techniques.

In one embodiment, the light source controller 210 causes thelight-sensitive tissue 201 to be illuminated by substantially whitelight for a first period of time and to be free of illumination from theillumination system for a second period of time thus providing thereduced amount of light within said preselected frequency range. Forexample, the light source controller 210 turns LEDs in the light source208 on and off. The light source controller 210 further communicateswith the imaging system 204 such that image acquisition is performedduring the first period of time while the light-sensitive tissue 201 isbeing illuminated with substantially white light and image acquisitionis stopped for the second period of time. The white light illuminationcan be performed by turning on red, green and blue LEDs in the lightsource 208, for example. In the example of FIG. 2, red, green, yellowand blue LEDs are used to obtain a better quality white-light spectrum.The image display system 206 displays an image of the portion of thelight-sensitive tissue 201 based on the image acquisition over the firstperiod of time to extend over both the first and second periods of timeto appear more like an uninterrupted white-light-illuminated image. Inother words, the image acquisition can be synchronized with the periodsof illumination according to one embodiment of the current inventionsuch that it appears that the light-sensitive tissue 201 is beingcontinuously illuminated; however, in actuality, there is noillumination over portions of the observation period. In someembodiments, the light source 208 can also include one or more LEDs toemit infrared light. This can be useful for imaging the infrared lightand/or for observing fluorescent light from a fluorescent dye introducedfor particular imaging purposes, for example.

The imaging optical detectors 218 of the imaging system 204 can includea plurality of optical detection elements that each having a spectralsensitivity that substantially matches a spectral emission of acorresponding one of said plurality of light-emitting diodes. Forexample, the imaging optical detectors 218 may include detectionelements that are each optimized to detect one of red, green, yellow andblue light corresponding to the emission spectrum of the light source208. In addition, in cases in which the light source includes aninfrared emitter, the detection elements of the imaging opticaldetectors 218 can be optimized to detect infrared light of thefrequencies emitted by the infrared emitter. When the sensitivity of thedetection element is relatively good at a frequency of relatively strongemission of an emitter, we can say the detection element and the emitterare substantially matched.

In another embodiment, the light source controller 210 causes thelight-sensitive tissue 201 to be illuminated by substantially whitelight for a first period of time and to be illuminated by substantiallyred light for a second period of time thus providing the reduced amountof light within the preselected frequency range. (One should notehowever that this does not have to be only red light. It could be anyillumination spectrum having lower phototoxicity than white light. Redis chosen as one embodiment because it is the least phototoxic of allvisible wavelengths. However, IR and other combinations of visible lightcould be used as well.) For example, the RGYB LEDs in the embodiment ofFIG. 2 could all be turned on for a period of time followed by a periodin which the red LED is turned on. The light source controller 210communicates with the imaging system 204 such that imaging data acquiredduring the first period of time while being illuminated withsubstantially white light is used to compensate imaging data acquiredover the second period of time such that the image displayed by theimage display system appears more like an uninterruptedwhite-light-illuminated image. Clearly, other embodiments of the currentinvention could include various combinations of periods of white light,red light and no illumination. In this embodiment, the imaging system204 can further perform the compensation such that at least one ofbackground image portions or images of tools within the image aresegmented from the light-sensitive tissue. Such background portions ofthe image and/or objects within the field of view such as tools that arebeing used during surgery, for example, can make it more difficult foran observer when view in monochromatic imaging, Therefore, segmentationand colorization can be performed, for example using PC or computer 222,according to some embodiments of the current invention.

In other embodiments, The light source controller 210 causes thelight-sensitive tissue to be illuminated by light having a reducedamount of light at wavelengths that are harmful to the light-sensitivetissue 201 relative to a white light spectrum, and the imaging system204 (e.g., using PC or computer 222) is adapted to apply a color boostmodel to compensate for the reduced amount of light at wavelengths thatare harmful to the light-sensitive tissue 201. For example, the blue LEDof light source 208 could be turned on for a shorter period of time thanthose of the other colors, or made less bright than it typically wouldbe for a well-adjusted white light source, to reduce the amount of lightat wavelengths that are harmful to the light-sensitive tissue 201. Insome embodiments, filters could be used instead of, or in addition to,the above-noted mechanism, for example. The color boost model can be orinclude companding, for example.

Examples

In order to implement examples of phototoxicity reduction techniquesaccording to some embodiments of the current invention, the illuminationsystem is provided with a tunable color spectrum and rapidturn-on/turn-off light emission. To satisfy these requirements, anLED-based solution has been chosen with red, green, blue, and yellow LEDchannels. However, the general concepts of the invention are not limitedto this particular example. Other types of light sources could be usedas well as other combinations of LED spectral properties. Furthermore,infrared LEDs could also be included for imaging and/or used withfluorophores, for example. According to this embodiment, independentcontrol of each color channel provides highly tunable color temperatureand excellent color rendering index (CRI).

Example System Overview

The heart of the illumination system according to this example is theLight Source Controller (LSC), which controls low-level modulation ofthe LEDs and synchronizes illumination activity with video camerashuttering. The computational power of the LSC is provided by a PIC24Fmicrocontroller; further detail concerning the LSC electronics design isprovided in provisional application Ser. No. 61/325,647 to which thecurrent application claim priority and the entire contents of which arehereby incorporated by reference. The LSC's illumination andsynchronization settings are controlled from a PC, which communicateswith the LSC via either USB or serial port (RS-232). A user controls theillumination system using a graphical user interface (GUI) applicationrunning on a PC. Alternatively, the illumination system may becontrolled by any other autonomous, semi-autonomous, or user-driven PCapplication that sends appropriate commands to the LSC. Appendix A inprovisional application Ser. No. 61/325,647, the entire contents ofwhich are hereby incorporated by reference, also describes theparticular software architecture for the system, including firmware forthe LSC's embedded electronics and the PC application for user levelcontrol.

The LSC is an embedded, stand-alone device that modulates four LEDcontrol channels and synchronizes camera shuttering. Illumination ofeach LED channel is controlled via pulse width modulation (PWM) using aPWM period of 100 microseconds. Adjustment of the PWM duty cyclesprovides independent brightness control over each LED channel. The LSCsynchronizes illumination and camera shuttering using a triggeringsignal sent at the start of capture for each video frame. This triggermay function either as an output, where the LSC is the trigger sourceand initiates frame capture, or as an input, where the camera is thesource of the trigger, which it sends at the beginning of each videoframe. When the trigger functions as output, the LSC sends the triggerat the start of a new frame and activates the LEDs. The LSC then waitsfor the shutter period of the camera to expire, at which point itdeactivates the LEDs. The LEDs remain off until the frame period hasexpired; this process then repeats for the next frame. During thisprocess, the camera will not begin capture of a frame until it receivesthe trigger signal from the LSC. Thus, the LSC has complete control overthe video frame rate. Similarly, when the trigger functions as input,the camera controls its own frame rate, sending the trigger signal tothe LSC at the start of each frame. When the LSC receives this trigger,it illuminates the LEDs until the shutter time of the camera hasexpired. At this point, the LSC deactivates the LEDs and waits for thenext trigger signal from the camera. By controlling/monitoring when eachvideo frame begins, the LSC ensures perfect synchronization betweenillumination activity and camera shuttering.

Although the illustrated LSC supports four LED channels, theillumination system easily may be extended to include any number of LEDchannels. This is done by using multiple LSCs and daisy-chaining thecamera trigger. Daisy-chaining the trigger is necessary in order tosynchronize all LSCs and the camera to the same trigger source. In thistopology, all but one of the LSCs (call this the master LSC) will settheir trigger as input. The master LSC may then either set its triggeras output, in which case it becomes the trigger source for the cameraand all remaining LSCs, or it may set its trigger as input, in whichcase the camera must source the trigger to all LSCs. To support thisdaisy-chain feature, an LSC is equipped with two identical triggerconnectors that are internally wired together. Thus, all that isrequired is to connect the camera to one of these connectors on themaster LSC and daisy-chain an additional LSC using the other connector.This chain can be extended from the added LSC to yet another LSC andso-on without limit.

Any LED of suitable current rating (refer to Appendix B of 61/325,647for specs) may be used with the LSC. In practice, any LEDs of anycurrent rating may actually be used by adjusting the current rating ofthe LED driver electronics used in the light source controller. The LEDsplug into a connector at the back of the LSC; thus, one LEDconfiguration may be readily swapped for another. The LEDs chosen forthis example are ACULED VHL LEDs from Perkin Elmer Optoelectronics inthe RGYB configuration (having an LED of color red, green, yellow, andblue). This product uses chip-on-board technology to package four veryhigh lumen LEDs onto a single chip. Having all four LEDs on one chipprovides an advantage of excellent color mixing due to the closeproximity of one LED to another.

Light from the LEDs may be delivered to the target via a variety ofmethods. The simplest method is to shine the LEDs directly onto thetarget. For this method, mounting a short fiber bundle rod directlyabove the LED chip helps focus the light and achieve optimal colormixing. Shining the LED directly over the target is not always the mostconvenient method, however, and may not even be feasible forapplications such as retinal surgery. Another method for retinal surgeryapplications is to mount the LEDs at the optical input to the surgicalmicroscope. Another method for retinal surgery, frequently preferred bysurgeons, may be to couple the light into a fiber-optic light pipe,which is then inserted through the sclera of the eye. Much light istypically lost in the process of coupling light into a small fiber. Thisis complicated by the very wide angle of divergence of light emittedfrom the ACULED LEDs. Thus, intense light brightness is required. Atypical light intensity for commercial fiber-optic light sources forretinal surgery is 10 lumens of light output from the fiber at maximumintensity (Chow, David, MD. “Shedding Some Light on CurrentEndoillumination: Brighter Light can be Safe Light”. Retinal Physician.January 2005. Retrieved fromhttp://www.retinalphysician.com/article.aspx?article=100050). Themaximum output of an ACULED VHL RGYB LED is 189 lumens (Perkin ElmerOptoelectronics. ACULED® VHL™ Standard White, Monochromatic andMulti-Colored Four-Chip LED Products. 2008). Numerous approaches toaccomplish capturing enough light into a fiber have been used and othersare anticipated. The specific method chosen to couple light into thefiber is not an element of this invention, and any method known in theart may be used, so long as sufficient light is provided to the imagingsystem for the purpose of acquiring images. For some applications, ithas not been necessary to have brightness equivalent to commercialsystems while using a light pipe; thus, somewhat lower brightness levelsmay been tolerated. Using other methods for light delivery, such as themicroscope's optical input, provides much higher brightness because ofthe wide diameter of the optical channel. This method has proven quiteadequate for some applications.

Two video cameras mounted on a surgical microscope provide ahigh-resolution video feed in 3D. Any camera supporting suitabletriggering capability could be used. For this setup, the Flea2 FL2-08S2CFireWire cameras from Point Grey Research are used, which provide aresolution of 1024×768 at 30 Hz. The video is displayed to the user in3D using a 3D-capable LCD screen; viewing the scene in 3D requires theuser to wear special polarizing glasses made for this purpose.

The techniques employed in these examples to accomplish phototoxicityreduction while using this illumination system are now discussed.

Phototoxicity Reduction Technique #1: Camera Shutter Synchronization

The simplest method employed by the illumination system to reducephototoxicity is to switch off the LEDs throughout the period when thevideo camera's shutter is closed. The shutter of a video camera istypically open for only a fraction of each video frame period. Theshutter time is typically controllable by the user and can be set to anydesired value. The shutter period must be long enough to capture enoughphotons to produce a clear, bright image, but not so long that pixels inthe image saturate or image blurring occurs due to motion in the scene.Even for extended shutter times, the shutter will typically close priorto the end of the frame period in order to allow time for data transferof the captured image (certain triggering modes on some cameras allowimage capture and data transfer to occur simultaneously).

As an example case, consider a video stream running at 30 Hz; the timeinterval between each frame is 33 milliseconds. If the shutter period isaround 15-20 milliseconds (a typical setting for good image quality inour experience) then the shutter is closed for about 50% of the frameperiod. Disabling illumination during this period reduces light outputby the same fraction. For slower frame rates, the fractional reductionin light output is even more dramatic. Provided that the relationshipbetween phototoxicity and total light exposure is approximately linear,this scenario would reduce phototoxicity by about 50%. We are currentlyconducting phototoxicity trials to determine what the relationshipbetween light output and phototoxicity really is in practice.

To aid understanding of the illumination system's operation, FIG. 3shows the LED and camera trigger waveforms corresponding to thisphototoxicity reduction technique. The figure assumes a frame rate of 30Hz. As seen in the figure, LED brightness is controlled using a PWMperiod of 100 microseconds. Thus, 333 PWM periods comprise one completeframe time of 33.3 milliseconds. The LEDs are only active during theopen shutter period of the camera, which in this example occurs duringthe first 15 milliseconds of each frame. The LEDs are shown operating atdifferent duty cycles: red at 50%, yellow at 50%, green at 75%, and blueat 25%. As a point of note, when two LEDs have the same duty cycle itdoes not necessarily mean they output the same brightness. In this case,they output the same fraction of their maximum brightness, but themaximum brightness is typically much different for LEDs of differentcolor because of the differences in material properties used to createLEDs of different color. The luminal output of the LEDs used for thisresearch is published in the ACULED VHL datasheet. Also notice in thefigure that the camera trigger signals the start of each frame period.

Phototoxicity Reduction Technique #2: Multiplexed Spectrum Imaging

The second technique used to reduce phototoxicity is to use varyinglight spectrums to illuminate consecutive video frames in repeatingsequence. Because the LSC knows when each frame capture begins, it canchoose different light spectrums to illuminate different frames. The waythis method reduces phototoxicity is by interleaving frames illuminatedby a white light spectrum in between frames illuminated by a red lightspectrum. White light contains all visible wavelengths and is thereforehighly phototoxic; red light, on the other hand, has very lowphototoxicity. Alternatively, IR light could also be used in place ofred light for even lower phototoxicity. FIG. 4 demonstrates an examplevideo sequence showing one frame illuminated by white light for everyfour red light frames.

As seen in FIG. 4, images captured under a light spectrum other thanwhite light contain incomplete color information. Some method istherefore required to restore color to images captured under the lowphototoxicity spectrum. Without this, the video feed would flickerbetween full-color and red-color images in a very distracting manner.Color flicker is eliminated from the video feed by mapping colorinformation from the most recent white light image to all subsequentimages captured under a low phototoxicity spectrum (i.e. red light).White image frames are displayed with no alteration, since they containcomplete color information. When displaying red images, the algorithmworks by registering the image background of the red image with thebackground of the last acquired white light image. Foreground objectsare also segmented from the images and tracked. Color information isthen mapped from the background of the white light image tocorresponding pixels in the background of the red image. This rendersthe image background in full-color. Foreground objects in the red image,such as surgical tools, are colored using a tool model, since thecorresponding pixels for these objects are harder to identify and maynot even be present in the white light image. The end result is a videofeed rendered in continuous full-color from a raw video stream havingalternating full-color and red-color images. The registration, tracking,and color mapping algorithms employed for this technique are furtherdescribed in detail in a paper presented at the 2010 IPCAI conference(Sznitman, R., Billings, S., Diego, R., Mirota, D., Yang, Y., Handa, J.,Gehlbach, P., Kang, J., Hager, G., Taylor, R., Active MultispectralIllumination and Image Fusion for Retinal Microsurgery. The JohnsHopkins University. Information Processing in Computer AssistedInterventions (IPCAI) Conference, Geneva, June 2010, the entire contentsof which are incorporated herein by reference). Using this phototoxicityreduction technique, the damaging impact of highly phototoxicwavelengths is reduced while still preserving the color information thatthese wavelengths provide.

The LSC provides a command for specifying an interval at which a whitelight spectrum is to be intermixed with a low phototoxicity spectrum.This interval is referred to as the “dark frame interval”. The darkframe interval sets the number of sequential frames to be illuminated bythe low phototoxicity spectrum following each frame illuminated by whitelight. Thus, a dark frame interval of zero results in all frames beingilluminated by white light. A dark frame interval of one results inevery other frame being illuminated by white light, reducing white lightexposure by 50%. Larger dark frame intervals provide progressivelygreater reduction in white light exposure, thereby reducingphototoxicity. The video sequence shown in FIG. 4 has a dark frameinterval of four. Typically, the dark frame interval is set by the uservia the GUI application. This interval could also be dynamically updatedusing an algorithmic-based solution. For example, the color mappingalgorithm may be programmed to adjust the dark frame interval based oncertain metrics of image quality.

Image Recoloring Examples

From the device described above, white and red light images arecyclically produced at a fixed rate. Naturally, emitting fewer whitelight images allows for lower levels of phototoxicity for the patient.However, reducing the number of white light images increases thedifficulty of the procedure for the surgeon. Hence, a method whichrestricts the number of white light images used, and still provides atypical view for the surgeon, can be provided. Ultimately, it is desiredto produce an accurate colored image of the retina at any given time,irrespective of which illumination was used.

To provide a coherent colored image sequence according to the currentembodiment, we present two methods: a naive and an active scenerendering approach. Due to the lack of previous work on this particulartopic, we treat the naive approach as a baseline algorithm. Thisalgorithm is simple and may be most useful only in cases with highfractions of white light. We also compare both methods on imagesequences where ground truth is available, thus demonstratingimprovements produced by non-naive methods.

At each discrete time step, t, we denote the type of illumination thedevice is triggering as L_(t) where L_(t)=1 when white light is used,and L_(t)=0 for non-white light. Associated with each illumination,I_(t)={R_(t), G_(t), B_(t)} is the corresponding RGB image acquired. Therate at which white light illuminates the retina is then defined as

$\begin{matrix}{\phi = \frac{\sum\limits_{i = 1}^{t}L_{i}}{t}} & (1)\end{matrix}$

In order to perform recoloring, it is necessary to correctly account forthe color of the non-white illuminant. We define the color space of theacquired images as the usual RGB color space denoted by S⊂R(3).Following (Mallick, S., Zickler, T., Belhumeur, P., Kriegman, D.:Specularity removal in images and videos: A PDE approach. EuropeanConference on Computer Vision (2006) 550-563), we define a separatecolor space S′⊂R(3) such that the color of the non-white illuminant is(1,0,0). We relate S and S′ by a linear transformation A of the formA=sR, where s is a scale factor and R is a rotation. Then for any RGBvalue p∈S, we can compute p′∈S′ as p′=A p. The optimal A can be computedby first acquiring a non-white illuminated image, finding the largestprincipal component, x, and subsequently constructing two orthogonalcomponents y and z as in (Mallick et al., ibid). R is constructed fromthese components. The scale s can then be computed by comparing a whilelight and non-white light image under the (color) rotation R.

Since our non-white illuminant is largely red, in the remainder of thisexample we will continue to refer to the non-white image as the “red”image and the two orthogonal components as green and blue with theunderstanding that these are, in general, not the raw color values fromthe camera.

We denote F_(t) as the final fully colored image rendered by our system.As the device sequentially provides us with images, we will maintain acolor model for the retina, M={m_(G), m_(B)}, where m_(B) and m_(G) arethe green and blue color models (represented in the form of images),respectively. Such a color model will be maintained over time, and wethus denote M, as the color model at time t. In order to have a colormodel at any given time, t, let I₁ be a white light image.

Naive Approach

Perhaps the simplest method to create and maintain a colored imagemodel, M, is to assume that images do not significantly change overtime. In other words, a strong continuity in the appearance in colorfrom I_(t) to I_(t+δt) is assumed.

The corresponding algorithm is simple: if L_(t)=1, then the model M_(t)is updated, M_(t)={G_(t), B_(t)} and F_(t)=I_(t). Otherwise, L_(t)=0 andwe let F_(t)=(R_(t), m_(G), m_(B)). Following such a procedures ensuresthat all F_(t) are fully colored images. FIGS. 5A and 5B show an exampleI_(t) and F_(t) respectively. Notice that continuity is violated, as thetool has been displaced since the last white-light image received, thuscausing “ghosting” effects.

Active Scene Rendering Approach

A natural extension of the naive approach is to infer the motionobserved in the image sequence and correct the associated artifacts. Wepresent our novel color fusing algorithm: Active Scene Rendering (ASR).Here the idea is to estimate the different forms of motion which appearin the scene and take this information into account when rendering thecolored images.

Here, it is still assumed that a strong temporal correlation betweenadjacent images is present. Furthermore, it is stipulated that atransformation, T, from image I_(t) to I_(t+1) can be inferred.Intuitively, T can be regarded as the motion, induced by the surgeon,which the eye undergoes during a procedure. Notice that thistransformation only accounts for the eye motion and not the tool motion.Hence, to further reduce colorization errors (as those in FIG. 5B), wemodel the tool and its motion as well. The idea is to first detect thepose of the tool to obtain a 2D segmentation and then use thisinformation to recolor the image correctly. We now describe how theestimation of the transformation T and the 2D tool segmentation areperformed.

Image Stabilization. As previously mentioned, the surgeon is free tomanipulate the eye. To compensate for this motion, a simple translationmodel for the motion of the retina is assumed. Although it has beenshown that wide angle retinal image deformation is best modeled with aquadratic deformation (Stewart, C., Chia-Ling, T., Roysam, B.: Thedual-bootstrap iterative closest point algorithm with application toretinal image registration. Medical Imaging, IEEE Transactions on 22(11)(November 2003) 1379-1394), small motion can be approximated with puretranslation when under high magnification. To estimate the translationwe first extract SIFT features (Lowe, D.: Distinctive image featuresfrom scale-invariant keypoints. International Journal of Computer Vision20 (2003) 91-110) (I_(t) is treated as a gray scale image for any valueof L_(t)), find correspondences and then apply the robust ASKC method(Wang, H., Mirota, D., Hager, G.: A generalized kernel consensus basedrobust estimator. IEEE Transactions on Pattern Analysis and MachineIntelligence 32(1) (2010) 178-184) to find the translation that bestexplains the correspondences. This permits us to find a transformationregardless of whether the tool is present in the image or not. Note thatin order to present coherent image sequences, images are cropped byremoving border regions.

Tool Detection. Given that the most consistent clue for the tool is itsconstant and known 3D shape, we use the framework proposed in (Rother,D., Sapiro, G.: Seeing 3D objects in a single 2D image. InternationalConference on Computer Vision (2009)) for simultaneous segmentation andpose estimation which exploits this information. This frameworkrequires, as input, the 3D shape (represented as voxel occupancies) andcolor probability distribution (represented as a mixture of Gaussians)of the tool, and the color probability distribution for each backgroundpixel (represented as a single Gaussian). The output of the framework isa segmentation of the tool in each frame, and also an estimate of the 3Dpose of the tool in the 3D coordinate system of the camera, for eachframe. The estimated 3D pose in one frame is used to initialize thesegmentation and pose estimation in the following frame. Using thismethod guarantees finding the globally optimal 3D pose and segmentationin a computationally efficient manner.

The algorithm for ASR is similar to that of naïve approach describedabove. At t=1, we let F₁=I₁ set M₁={G₁, B₁}. I₁ is then treated as theinitial frame of reference, such that subsequent images are stabilizedwith regards to I₁. That is, for every new image I_(t), we compute thetransformation T_(t) from I_(t) to I₁. Then, using T_(t), we translateI_(t) and compute a rectified image, Ĩ_(t). When L_(t)=1, we setM_(t){{tilde over (B)}_(t), {tilde over (G)}_(t)} and F_(t)=Ĩ_(t).

If L_(t)=0 (FIG. 6A), the 2D segmentation of the tool is determined(FIG. 6B). To do this, M_(t) and the known color model of the tool isused to initialize the detection process described above. Once thesegmentation has been computed, this region is rendered using the toolcolor model. The rest of the image is rendered as F_(t)=({tilde over(R)}_(t), m_(G), m_(B)) (FIG. 6C).

Experimentation

We now show how our system performs on phantom image sequences. Aquantitative comparison of both methods is described below, where it isshown that ASR surpasses the naive approach in a setting where groundtruth is known. This is shown by measuring the error for differentvalues of φ. Qualitative results of our system on image sequences arethen provided in the section that follows.

Validation with Ground Truth

To validate both approaches described above, we recorded two imagesequences of membrane peeling on embryonic eggs using only white light.Doing so allows us to synthetically generate limited-spectrum images atany rate φ, by using only the red band of white light images. Hence, weknow that the transformation A (see above) is known to be A=I. Asdetailed in (Leng, T., Miller, J., Bilbao, K., Palanker, D., Huie, P.,Blumenkranz, M.: The chick chorioallantoic membrane as a model tissuefor surgical retinal research and simulation. Retina 24(3) (2004)427-434, Fleming, I., Balicki, M., Koo, J., Iordachita, I., Mitchell,B., Handa, J., Hager, G., Taylor, R.: Cooperative robot assistant forretinal microsurgery. International Conference on Medical ImageComputing and Computer Assisted Intervention 11(2) (2008) 543-550), thisphantom setup provides a similar environment to in-vivo settings. Imagesequences consist of 500 images, acquired at 20 frames per second usingthe system described above. Using this data, 5 image sequences aregenerated where φ={½, ¼, ⅛, 1/16, 1/32}. For each sequence, both naiveand ASR colorization approaches are evaluated. Since the groundtruth—the original recorded white light images—is always available, anerror can be computed for each frame generated by either approach. Inthe following experiments the L2 (or mean squared error) norm is chosento measure the error between the ground truth and the rendered images.In addition, we also compute the error using the Bounded Variation (BV)norm, which has been used to quantify image quality during denoisingTasks (Chang, Q., Chern, I.: Acceleration methods for totalvariation-based image de-noising. SIAM Journal of Applied Mathematics25(3) (2003) 982-994). This provides us with a measure of image quality,taking into account both photometric and rectification errors.

FIG. 7A shows the L2 norm error when varying φ for both methods. FIG. 7Bshows a similar result for the BV norm. In general, one can observe thatas φ decreases, the error rate increases. This is expected as theassumption of similarity between frames, discussed above, isincreasingly violated. Naturally, the naive approach suffers greatlywhen φ is small, as the true color may differ greatly from the lastobserved color. ASR however suffers significantly less from small φvalues, as it is able to maintain a more accurate color model.

Egg Peeling

Now that we have observed that ASR can provide a better way to modelretinal-type scenes, we set up our system to record and display imagesfor different values of φ. We record several image sequences in asimilar setup as above and show the resulting recolored sequence. Notethat the color mapping transformation A is assumed to have R=I, and auniform scaling factor (determined empirically).

In FIG. 8 we show a typical image sequence (φ=½), of a chorioallatonicmembrane peel from an 11 day old chicken embryo. The resulting recoloredimages rendered by our system are shown. A video recording of thissequence was made. The video shows four similar peeling sequences whereeach row corresponds to a different φ value (½, ¼, ⅛, 1/16). The firstcolumn shows the images provided by the device, while the second andthird columns show how the naive and model approach, respectively,render the sequence. Since the device is being used to obtain theseimage sequences, no ground truth is available for quantitativecomparison. Notice that in general the model approach renders a morecoherent image than the naive approach. This is particularly true atsmaller values, concurring with the results above.

In this example, we have conducted experiments with a novel systemaccording to an embodiment of the current invention that can be used toreduce toxic light exposure in retinal microsurgeries. The systemincludes a new lighting device which can reduce emission of highly toxicwavelengths. In addition we have developed a novel algorithm that can beused with this device in order to render a fully colored image sequenceto the user, thus avoiding visual discomfort. We have shownqualitatively and quantitatively that our method can provide superiorrendering over naive approaches. Even at low φ rates (e.g. ⅛ or 1/16),we showed that maintaining high color fidelity is possible, allowing forlow levels of phototoxicity. However, most retinal surgeries involvechanging the structure of the retina, and hence the color of the retina(as described in Sznitman, R., Lin, H., Manaswi, G., Hager, G.: Activebackground modeling: Actors on a stage. International Conference onComputer Vision, Workshop on Visual Surveillance (2009)). As seen in ourimage sequences, regions of the retina which are altered by the surgeoncannot be recolored correctly until a new white light image is providedaccording to this embodiment. Hence a potential improvement of thismethod would involve a dynamic q′, which could change as a function ofthe activity in the image sequence.

Although phototoxicity reduction in retinal surgery provided themotivating focus for this experiment, our technical approach to theproblem is potentially more broadly applicable. We have developedmethods for actively controlling the illumination spectrum in videomicroscopy and endoscopy and for fusing the resulting image sequences toform continuous and coherent image sequences. These methods areapplicable in many clinical applications, including neurosurgery andcancer surgery. For example, changing the illumination spectrum can beused to improve tissue contrast or discrimination or the depth ofpenetration of light into tissue structures. The methods proposed inthis paper may be adapted to such cases while still giving the surgeonmore options on the actual visualization.

Phototoxicity Reduction Technique #3: Color Companding of PhototoxicWavelengths

The final technique of these examples for reducing phototoxicity is toperform companding of color information that corresponds to highlyphototoxic wavelengths within the illumination spectrum. This is done byreducing the intensity of highly phototoxic wavelengths to somefractional value while keeping the wavelengths of low phototoxicity atnormal intensity. The result is an image with weakened color informationat colors corresponding to the attenuated wavelengths. Using a colorboost model, this weakened color information is restored to normallevels by computationally boosting the color response of the affectedwavelengths by an amount proportional to the magnitude of attenuation.

The trade-off for this approach is that the granularity of the boostedcolor information is less precise, which results in increasingly largestep sizes in color value as the boost magnitude increases. Theincreased step sizes only affect the color channels being boosted,however. For example, if blue light emission is reduced and the blueimage color correspondingly boosted by a factor of two, the step size inblue pixel values will increase from one to two, while the step size inred and green pixel values will remain, for the most part, unaffectedwith a value of one. The gain in phototoxicity reduction by thistechnique can be dramatic. With this technique, images can be capturedunder an illumination spectrum comprised of an intensity gradient thatis weighted according to the phototoxicity of each constituentwavelength. Wavelengths of low phototoxicity can illuminate at highintensity, providing fine-granularity color information, whilewavelengths of high phototoxicity illuminate at diminished intensity,providing large-granularity color information after boosting. Using lowboost values, such as two, provide almost unnoticeable effect on imagequality while offering potentially drastic reduction in phototoxicity.

A simple linear model that maps the illumination intensity of each LEDchannel to its corresponding camera response in RGB pixel value is onepossible approach. This model assumes a linear camera response, i.e.doubling the intensity of an LED channel corresponds to doubling thepixel value response in the captured image. At the time this model wasdeveloped for this example, only red, green, and blue LEDs were used forillumination (the yellow channel had not yet been added).

The color boost model is represented by equation (2). X is a 3×1 vectorcontaining the illumination intensity (range [0,1]) of each LED channel.In this case, three LED channels have colors red, green, and blue. C isa 3×1 vector representing a color-boosted RGB pixel value in the videoimage (range [0,255]). K is a 3×3 matrix mapping illumination intensityof each LED channel to its corresponding camera response in pixel value.λ is a 3×3 diagonal matrix containing the boost parameter for each LEDchannel. Under normal, non-boosting conditions, λ, is the identitymatrix. Equation (3) shows the same equation, but with each elementexpanded into its constituent sub-elements.

$\begin{matrix}{C = {K\;\lambda\; X}} & (2) \\{\begin{bmatrix}C_{R} \\C_{G} \\C_{B}\end{bmatrix} = {{\begin{bmatrix}K_{R,R} & K_{G,R} & K_{B,R} \\K_{R,G} & K_{G,G} & K_{B,G} \\K_{R,B} & K_{G,B} & K_{B,B}\end{bmatrix}\begin{bmatrix}\lambda_{R} & 0 & 0 \\0 & \lambda_{G} & 0 \\0 & 0 & \lambda_{B}\end{bmatrix}}\begin{bmatrix}X_{R} \\X_{G} \\X_{B}\end{bmatrix}}} & (3)\end{matrix}$

The K matrix is camera-dependent and is determined experimentally in acalibration step. This calibration is done by illuminating a white sheetwith one LED channel at a time set to maximum brightness. The averagepixel value from the camera image becomes the camera response for thatLED channel. For example, when the red LED channel is illuminated, thecamera response is [219, 7, 0]^(T) in terms of RGB values. This vectorcomprises the first column of matrix K. Similarly, the camera responseto the green and blue channels form the remaining columns of matrix K.The complete calibration matrix K for the ACULED VHL LED light sourcewith red, green, and blue LEDs and the Flea2 Point Grey Research camerasused in one embodiment of our system is shown in equation (4) below:

$\begin{matrix}{K = {\begin{bmatrix}219 & 2 & 1 \\7 & 136 & 76 \\0 & 26 & 184\end{bmatrix}.}} & (4)\end{matrix}$

As an example scenario using this phototoxicity reduction technique,consider the case of reducing the blue LED intensity to 50%. In order tomaintain the same apparent color balance in the video image, the boostvalue λ_(B) for the blue LED is set to two. However, the model we havefrom equation (2) is not yet in the needed form to compute the colorboost. C is the value we wish to calculate for each pixel; K and λ areknown. What remains is to determine X for each pixel. However, the rawpixel values from a captured video image do not provide X; rather, thesepixel values correspond to the value that C would be given no colorboost (call this value C_(REAL)). This makes intuitive sense because thecolor boost is a computational step following image capture. At thepoint of image capture, the image represents the real pixel values whichintuitively have no applied color boost. What we must do is determine Xfrom our model based on the assumption that the C_(REAL) we are givenhas no boost factor, i.e. assuming X is the identity matrix. Then we canplug this X into our boosting model and calculate a new C with thedesired boost parameters applied.

According to the model in equation (2), λX is equivalent to K inverseapplied to C. For the pixel values in the raw image, we know λ isidentity and C is also known as C_(REAL), the value of each pixel in theimage. Thus, we have that X is equal to the value shown in equation (5).Because we know K and we know C_(REAL), we now know X for our boostmodel.X=K ⁻¹ C _(REAL)  (5)

Substituting equation (5) for X in the boost model leads to equation (6)for calculating a new C adjusted according to the boost parameters. Thisnew C becomes the new pixel value in the color-boosted image.C=KλK ⁻¹ C _(REAL)  (6)

In implementing this technique, the λ term in the model may beautomatically updated based on the LED intensities set by the user.Because the PC application knows the intensity setting for each LEDchannel, λ can be automatically calculated to achieve consistent colorbalance in the image. Thus, the user may change the color balance atwill, while the algorithm automatically adjusts the boost to maintainuniform color balance. Typically, a user would reduce the blue and greencolor balance relative to red while observing the resulting imagequality as the algorithm attempts to keep the color balance consistent.In this way, the user may reduce the harmful wavelengths to as low asetting as possible while still preserving an image of satisfactoryquality.

As an alternative to the scheme provided above, instead of using a colorboosting model to recalculate pixel values in the image, the camera'sbuilt-in white balance may be adjusted to correct for changes in therelative illumination intensities of different wavelengths. This wouldrequire another model to predict the optimal white balance settingsdependent on the relative intensities of each color channel.

Phototoxicity Reduction Technique #4: Adaptive Spectrum Imaging

In order to provide the surgeon with accurately colored images whenusing the light source such as in the example above, we present analgorithm that dynamically chooses which illumination type to use ateach time step, depending on estimates of the rendered image quality andphototoxicity levels induced. That is, the quality of the recolorizationand phototoxicity levels are continuously monitored, allowing us toestimate when it is appropriate to use white light illumination. Ingeneral, this occurs when the scene changes cannot be adequately“predicted” with the current available information.

The system we use includes a device capable of illuminating the retinausing either white light, or less phototoxic red light as describedabove. We define the sequence of images provided by the system as I={I₁,. . . , I_(N)} for N discrete time steps. Each image I_(t) is associatedwith a particular illumination L_(t), where L_(t)=1 means that whitelight was used at time t, and L_(t)=0 means that red light was used.Consequently, when L_(t)=1 all three color channels are available,I_(t)={I_(t) ^(R), I_(t) ^(G), I_(t) ^(B)}, whereas when L_(t)=0 onlythe red channel I_(t) ^(R) is available. We define the illuminationhistory as Lt={L₁, . . . , L_(t)}. As in the example above, the overallrate at which white light is defined as in equation (1). We denote byF_(t) the final fully colored image rendered by our algorithm. Torecolor the monochromatic images we maintain a color model of the scenefor each time t, M_(t)=(M_(t) ^(R), M_(t) ^(G), M_(t) ^(B)).

Our goal then is to choose which illumination type, L_(t+1), to use forthe next time step. To do this, our criterion is to maximize aquantitative estimate of the patient's wellbeing. This criterioncombines the two costs incurred by the patient at time t: the “surgeonimpairment cost” and the “phototoxicity cost”. The surgeon impairmentcost, S(ε_(t)), is the cost of being accidentally harmed by the surgeonbecause of the error levels present in the recolored images, ε_(t). Thephototoxicity cost, T(L_(t)), is given by the damage to the patientproduced by the illumination. In the next section we describe thesecosts in more detail. In the section after that, we show how these costsare combined to select which illumination type to use at each time step

Modeling the Cost Functions

As described in the previous section, there are two different costsincurred by the patient at time t during the procedure. The first costis the “surgeon impairment cost”, S(ε_(t)). This is the cost (for thepatient) of being accidentally harmed by the surgeon at time t. Clearlythis risk (and hence the cost) increases as the recolorization error,ε_(t) (defined below) increases, since the surgeon is relying on poorerimages to perform his job. The exact relationship between this cost andthe error is unknown and depends, among many things, on the particularsurgeon using the system. However we expect S(ε) to be an increasingfunction that levels off at a certain error, ε*, at which stage thequality of the image is so poor that further deterioration does notresult in additional risk. In practice, we will make sure that thesystem remains in the linear part of S, far from the critical value ε*,where the surgeon is critically impaired. Based results from theexamples above, we will model this relationship with the followingfunction,

$\begin{matrix}{{S(\epsilon)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu}\epsilon} > \epsilon^{*}} \\\frac{\epsilon}{\epsilon^{*}} & {otherwise}\end{matrix} \right.} & (1.1)\end{matrix}$

The recolorization error, ε, is due to the fact that the color model atany given time is not perfect, since the background scene changes due tothe manipulations performed by the surgeon. In order to compute thiserror, we note that errors are only committed in the green and bluechannels, since the red channel is observed at all times. We assume thatthe error committed in the green and blue channels at time t, ε^(G,B)_(t), is approximately equal to the error that would be obtained in thered channel, ε_(t) ^(R), if it were treated as the green and bluechannels (ε^(G,B) _(t)≈ε^(R) _(t)). Since the red channel is availableat all times irrespective of the illumination type, ε^(R) _(t) can bedirectly computed as,ε_(t) ^(R) =∥M _(t) ^(R) −M _(t) _(w) ^(R)∥₂  (1.2)

where t_(w) is the last time step in which L_(tw)=1. Assuming furtherthat the error does not change significantly in one time step, weapproximate the error at time t+1 by the error at time t, hence ε^^(G,B)_(t+1)≈ε^^(G,B) _(t)≈ε^(R) _(t).

The second cost, the “phototoxicity cost,” T(L_(t)), is the estimateddamage at time t suffered by the patient because of the illuminationused up to this point in time L_(t). It seems reasonable from thecurrent literature (Ham, W. J., Mueller, H., Ru olo, J. J., Guerry, D.,Guerry, R.: Action spectrum for retinal injury from near-ultravioletradiation in the aphakic monkey. Am J Ophthalmol 93 (1982) 299-306) torelate the amount of phototoxic damage, T, to a function of the recentlight exposure φ(L_(t)), where φ(L_(t)) is a function that models howthe illumination history L_(t) affects a cell at time t. We chose todefine φ(L_(t)) as an exponential loss (approximated from Ham et al.,ibid). That is, as time goes on, the influence of the past decreasesexponentially fast. Hence, we approximate the phototoxicity cost by,

$\begin{matrix}{{T\left( L_{t} \right)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu}{\varphi\left( L_{t} \right)}} > L^{*}} \\{\mathbb{e}}^{\frac{- {({{\varphi{(L_{t})}} - L^{*}})}^{2}}{2}} & {otherwise}\end{matrix} \right.} & (1.3)\end{matrix}$

where L* is some level of illumination at which irreversible damage tothe patient (cell death) is produced.

It must be noted that while the choice of these functions is based onreasonable assumptions, these functions ultimately need to beempirically determined.

Choosing the Next Illumination Type

We can then formally define the estimated total cost for the patient attime t+1 as the sum of the two costs described in the previous section,E(L _(t+1),ε^_(t+1))=(1−λ)S(ε^_(t+1))+λT(L _(t+1)).  (1.4)

where, ε^_(t+1), is the measure of the recolorization error defined inEq. 1.2, L_(t+1) is the history of illuminations at time (t+1) and λ isa tuning parameter which can be adjusted by the user (i.e. surgeon) tospecify a bias for either image quality or phototoxicity. We select thenext illumination type, by minimizing the patient wellbeing cost,L _(t+1) =arg min_(L) E(L _(t+1),∈^_(t+1))=argmin_(L){(1−λ)S(^_(t+1))+λT(L _(t+1))}.  (1.5)

Notice that L can take only two values (0 or 1). Hence, thisoptimization reduces to(1−λ)S(^∈_(t+1))+λ(T([L _(t);1])−T([L _(t);0]))≥0  (1.6)

Since ^∈_(t+1)=0 when L_(t+1)=1, and ^∈_(t+1)≈^∈_(t) when L_(t+1)=0, allthe quantities in Eq. 1.6 are known and choosing the next illuminationtype simply reduces to determining whether or not Eq. 1.6 is true.

Adaptive Active Scene Rendering

We now present the outline of our algorithm according to an embodimentof the current invention: Adaptive Active Scene Rendering (AASR). FIGS.9 and 10 provide a visual outline of AASR and associated images. Firstfor each image I_(t), (FIGS. 9(a) and 10(a)) we detect and segment thetool in the image by using a 3D tool model (see example above for moredetails). This provides us with a mask region for the tool, T_(t) (FIG.9(b)). Then, in order to compute the new color model: if L_(t)=1, M_(t)is computed by keeping pixel regions of M_(t−1) which appear where thetool is located and using I_(t) for regions where the tool is notpresent (FIG. 10(c)). This is done by using T_(t) to mask regions of thetool and allows for regions displaying the retina to be updated, keepingtool regions unchanged (similar to the work in Sznitman, R., Lin, H.,Manaswi, G., Hager, G.: Active background modeling: Actors on a stage.ICCV, Workshop on Visual Surveillance (2009) 1222-1228). If L_(t)=0,then M_(t)=M_(t−1) (FIG. 9(c)). Rendering the recolored image, F_(t), isthen done by combining and Pt on regions outside the tool, and using atool color model to fill in the tool ((FIGS. 10(b)) and 9(d)). Havingcomputed these, we can then estimate the error, ∈_(t), using M_(R) _(t)and M_(R) _(tw) , as described by Eq. 1.2 (FIG. 10(d)), and choosing thefollowing illumination type can be computed as in Eq. 1.6 (FIG. 10(e)).

Experiments

We now show how our system performs on image sequences from phantoms andfrom chorioallatonic chicken embryos. First, a quantitative comparisonof AASR and a state-of-the-art method is presented; where it is shownthat AASR surpasses ASR in a setting where ground truth is known. Thisis shown by measuring both image recoloring quality and quantity ofwhite light used. We then show qualitative results of our algorithm onimage sequences.

To validate the approach described above, we recorded 5 image sequencesof membrane peelings on phantom eyes using only white light. Eachsequence consists of approximately 300 frames in similar visualsettings. Doing so allows us to synthetically generate limited-spectrumimages at any given time, by using only the red channel of white lightimages. This provides us with a way to quantitatively compare AASR andASR, as ground truth is available.

For each image sequence we then ran AASR with three different settings:λ={0.25, 0.5, 0.75}. This allows us to see results for cases where thesurgeon applies a bias towards image quality, phototoxic levels, or nobias at all. For each image sequence, we also generated 4 recoloredsequences using ASR, with different values of φ={½, ¼, ⅛, 1/16}. As inthe example above, the L₂ (or mean squared error) norm is chosen tomeasure the error between the ground truth and the rendered images. Inorder to estimate phototoxicity levels, we observe the proportion ofwhite-light images used.

In FIG. 11A we show the results of this experiment by plotting theaverage recolorization error against the average estimated phototoxicitylevel. The dotted line (4 vertices; 1 for each value of φ) shows how ASRperforms while the full line describes the performance of AASR (3vertices; 1 for each value of λ). In general, we can notice that bothmethods displays a trade-off in accuracy: reducing one type of errorinduces the other and vice versa. We can also see that the AASR curvelies below that of ASR for every recolorization error level, henceachieving smaller total costs for the patient. In general, from ourcurrent experimental setup, AASR significantly outperform ASR, for thevalues of λ specified. Also, note that if all incoming images wereregistered to a reference frame (as in the example above) an additionalreduction in colorization error would be expected.

Having observed that AASR provides a better way to model retinal-typescenes, we now present results on a typical image sequence of achorioallatonic membrane peel from a 12 day old chicken embryo. In FIG.11B we show a small set of images from this sequence and the resultingrecolorization using AASR (λ=0.5). The original and recolored videosequence can be seen in a video included in the supplementary materials.In the video, the same peeling sequence is visible and each rowcorresponds to a different value for λ={0.25, 0.5, 0.75}. The firstcolumn shows the original images. The second column displays the imagesprovided by the device, while the third column shows the imagesrecolored by AASR. The last column displays the retina color model overtime. Other similar video sequences are provided in the supplementarymaterials.

Notice that in general, in image sequences which contain little membranemanipulations, few white light images are used. Since in this scenarioour prediction model is capable of correctly estimating the colors ofthe retina, few white light images are necessary. Conversely, frameswhich show membrane peeling require more frequent white lightillumination, in order to correctly render the colors. This indicatesthat the framework is able to choose which illumination type to usedepending on the surgeon's actions.

In this example we have presented a novel algorithm that can be used toreduce toxic light exposure during retinal microsurgery. When used withthe LED light source according to some embodiments of the currentinvention, our recoloring scheme can dynamically choose the illuminationbased on the circumstances, reducing potential light induced retinaltoxicity. Our algorithm balances the risks of phototoxic retinal damagewith the illumination requirements of the operating surgeon to performthe surgical tasks. In this example we provide qualitative andquantitative evidence that this novel method reduces the dose of light,and hence retinal damage, while maintaining sufficient illumination toexecute required surgical maneuvers safely.

While the results we have presented are in part dependent on themodeling choices of the cost functions, our framework is generic enoughto accommodate a large number of functions. This being said, a naturalfuture direction to im-prove the present work is to empiricallydetermine the specific forms of the cost functions to use. Determinationof these relationships would permit a truthful quantitative evaluationof the harm reduction. In ongoing and future work, we will be exploringthese issues.

When used individually, any of these phototoxicity reduction techniquescan easily reduce exposure of the most hazardous white light wavelengthsby at least 50%. When used in parallel, truly drastic reduction in whitelight exposure is possible. As a typical example, suppose we have avideo frame rate of 30 Hz and a typical camera shutter time of 16.5milliseconds. Applying camera shutter synchronization reduces all lightexposure to 50%. Setting the dark light interval to one reduces exposureto white light by another 50% down to 25% of the original. With a darklight interval equal to one, color mapping is very accurate and thus haslittle effect on video quality. Next, apply color companding by reducingthe blue LED intensity to 50% and applying a λ_(B) boost value of 2.This reduces blue light exposure by another 50%, totaling 12.5% of theoriginal blue light intensity. Applying a factor two color boost to theblue light response also has largely negligible impact on image quality,since the blue pixel value step size changes from 1 to only 2, dividingthe value range [0,255] into 128 possible values rather than 256.Meanwhile, color rendition for red and green color spectrums remain thesame. The resulting impact on image quality may not even be noticeableby the average user. In the end, blue light is reduced to 12.5%, greenlight to 25%, and red light to 50% of the original white lightintensity. Because phototoxicity primarily occurs within the bluewavelength range, the resulting light spectrum provides drasticallysafer illumination compared with no phototoxicity reduction. Further, byprocessing the images, the computer may adaptively adjust themultiplexing rate and/or the relative intensity of phototoxicillumination in color toxicity to provide only the minimum amount ofphototoxic illumination needed at any particular time in the procedure.Similarly, the surgeon may be provided with an explicit command methodsuch as a foot pedal or voice recognition system to explicitly adjustthe parameters of the various phototoxicity reduction methods or toselect different modes of operation of the system.

We have developed an illumination system that can be used for retinalsurgery that drastically reduces exposure to highly phototoxicwavelengths inherent to white light illumination. The illuminationsystem carries the potential to significantly impact retinal surgeryoutcomes by ridding many complications that result when the retina isdamaged by intense illumination during surgery. Since video monitoringis used for viewing the surgical field when phototoxicity reductionmethods are enabled, a change to the way retinal surgery is currentlyperformed may result. Instead of viewing the procedure through anoptical microscope, surgeons would use a video-based display. Usingvideo-based visualization for eye surgery may well become the preferredmethod of the future, as it can provide many benefits to the surgeon,including improved ergonomics and less physical fatigue resulting fromback and neck strain following long hours working at the microscope. Inaddition, the added potential to integrate information sources into thesurgeon's field-of-view is a further motivating factor towardsvideo-based surgery. Such information may include sources such assophisticated navigation and sensing aids, as well as preoperativeimaging data, such as fundus images.

A secondary benefit of the illumination system can include the abilityto tune the color temperature to not only reduce phototoxicity, but alsoto improve visualization when a certain color temperature providesbetter rendition of an object of interest. A further possible use of theillumination system can be to add special-purpose illumination channels,such as a channel that activates a fluorescent dye. The excitation phasecould be performed while the camera shutter is closed, so as not toalter the light spectrum used for visualization, for example. IR lightcould also be used for very low phototoxicity illumination or to seedeep into retinal tissue.

As a modification to the system described, the light source could bealtered to work with light sources other than LEDs, including Xenonlight sources which are the medical standard. This modification wouldnot allow for a tunable color temperature, but the light output couldstill be shuttered to correspond with the shutter and frame times of thecamera. A low phototoxicity spectrum could also be used in thisscenario, either by rapid switching to an alternative light source orthrough the use of dynamically interchangeable filters. Shuttering aXenon light source in this way could use a mechanical-based shutterdesign rather than the electronic-based approach taken with the LEDlight source.

The embodiments illustrated and discussed in this specification areintended only to teach those skilled in the art the best way known tothe inventors to make and use the invention. Figures are not drawn toscale. In describing embodiments of the invention, specific terminologyis employed for the sake of clarity. However, the invention is notintended to be limited to the specific terminology so selected. Theabove-described embodiments of the invention may be modified or varied,without departing from the invention, as appreciated by those skilled inthe art in light of the above teachings. It is therefore to beunderstood that, within the scope of the claims and their equivalents,the invention may be practiced otherwise than as specifically described.

We claim:
 1. An observation system for viewing light-sensitive tissue,comprising: an illumination system; an imaging system arranged to be inan optical path of light from said light-sensitive tissue upon beingilluminated by said illumination system, said imaging system comprisingan optical detector and a data processing system; and an image displaysystem in communication with said imaging system to display an image ofa portion of said light-sensitive tissue, wherein said illuminationsystem comprises a light source and a light source controllerconstructed and arranged to control at least one of a spectralcomposition and intensity of light that illuminates said light-sensitivetissue; wherein said light source controller causes said light source toilluminate said light-sensitive tissue with light having a reducedamount of light at wavelengths that are harmful to said light-sensitivetissue relative to a white light spectrum, wherein said imaging systemis configured to image at least a portion of said light-sensitive tissueupon being illuminated by said illumination system by applying a colorboost model to compensate for said reduced amount of light atwavelengths that are harmful to said light-sensitive tissue.
 2. Anobservation system according to claim 1, wherein said light sourcecontroller is configured to reduce an intensity of light of wavelengthsthat are harmful to said light-sensitive tissue while maintaining anintensity of light of wavelengths that are not harmful to saidlight-sensitive tissue as compared to white light illumination.
 3. Anobservation system according to claim 2, wherein said light sourcecontroller is configured to reduce an intensity of light of wavelengthsthat are harmful to said light-sensitive tissue by dividing an intensityof said wavelengths that are harmful to said light-sensitive tissue inwhite light illumination by a predetermined value.
 4. An observationsystem according to claim 3, wherein said imaging system boosts a colorresponse of the wavelengths that are harmful to said light-sensitivetissue captured by said optical detector by an amount proportional tosaid predetermined value to compensate for said reduced amount of light.5. An observation system according to claim 1, wherein said imagingsystem is configured to apply said color boost model using the equationC=KλX, where C is a vector representing a color-boosted pixel in saiddisplayed image, K is calibration matrix mapping an illuminationintensity of each of a plurality of wavelengths to a correspondingresponse in pixel value of optical detector, λ is a diagonal matrixcontaining a boost parameter for each of said plurality of wavelengths,and X is a vector containing an illumination intensity of each of saidplurality of wavelengths detected by said optical detector.
 6. Anobservation system according to claim 1, wherein said light sourcecomprises a plurality of light-emitting diodes and said light sourcecontroller is adapted to control an intensity of each of said pluralityof light-emitting diodes.
 7. An observation system according to claim 6,wherein said plurality of light-emitting diodes comprises at least onelight-emitting diode for emitting light in each of three primary colorregions of said white light spectrum.
 8. An observation system accordingto claim 6, wherein said plurality of light-emitting diodes comprises ared, a green, a yellow and a blue LED.
 9. An observation systemaccording to claim 6, wherein said plurality of light-emitting diodescomprises an infrared LED.
 10. An observation system according to claim6, wherein said imaging system comprises a plurality of opticaldetection elements, each having a spectral sensitivity thatsubstantially matches a spectral emission of a corresponding one of saidplurality of light-emitting diodes.
 11. An observation system accordingto claim 1, wherein said color boost model includes companding.
 12. Amethod of displaying an image of light-sensitive tissue, comprising:illuminating said light-sensitive tissue with light having a reducedamount of light at wavelengths that are harmful to said light-sensitivetissue relative to a white light spectrum; detecting light from saidlight-sensitive tissue upon being illuminated; imaging at least aportion of said light-sensitive tissue upon being illuminated byapplying a color boost model to said detected light to compensate forsaid reduced amount of light at wavelengths that are harmful to saidlight-sensitive tissue; and displaying said image of saidlight-sensitive tissue.
 13. A method according to claim 12, whereinilluminating said light-sensitive tissue with light having a reducedamount of light at wavelengths that are harmful to said light-sensitivetissue relative to a white light spectrum comprises reducing anintensity of light of wavelengths that are harmful to saidlight-sensitive tissue while maintaining an intensity of light ofwavelengths that are not harmful to said light-sensitive tissue ascompared to white light illumination.
 14. A method according to claim13, comprising reducing an intensity of light of wavelengths that areharmful to said light-sensitive tissue by dividing an intensity of saidwavelengths that are harmful to said light-sensitive tissue in whitelight illumination by a predetermined value.
 15. A method according toclaim 14, wherein applying a color boost model comprises boosting acolor response of the detected light of the wavelengths that are harmfulto said light-sensitive tissue by an amount proportional to saidpredetermined value to compensate for said reduced amount of light. 16.A method according to claim 12, wherein applying a color boost modelcomprises using the equationC=KλX, where C is a vector representing a color-boosted pixel in saiddisplayed image, K is calibration matrix mapping an illuminationintensity of each of a plurality of wavelengths to a correspondingresponse in pixel value of an optical detector detecting said light, λis a diagonal matrix containing a boost parameter for each of saidplurality of wavelengths, and X is a vector containing an illuminationintensity of each of said plurality of wavelengths of said detectedlight.